How do AI governance frameworks address third party and vendor AI risk?

Third party AI risk is consistently one of the weakest points in enterprise governance frameworks, largely because AI features embedded in vendor products often bypass the same level of scrutiny that internally built systems go through. When a vendor adds an AI powered feature to a tool an organization already uses, that feature can quietly begin processing sensitive data without triggering the same review process that a newly proposed internal AI project would face, simply because it arrived through an existing vendor relationship rather than as a new initiative.

Stronger governance frameworks address this by requiring vendor disclosure of any AI features embedded in their products, along with data handling attestations that clarify what happens to information processed by that AI functionality. Ideally, the same monitoring standards applied to homegrown agents get extended to cover vendor integrations as well, though this is harder to achieve in practice since organizations have less direct visibility and control over how a vendor's AI features actually behave internally.

The practical challenge is that vendor AI risk often goes unnoticed precisely because it does not look like a new AI deployment from the organization's perspective. It looks like a routine software update to a tool that has already been approved and is already in use. Akto's Atlas helps address this by discovering AI tool and agent usage across the enterprise broadly, including vendor embedded AI features that security teams might not otherwise realize are active, bringing this category of risk into the same visibility framework applied to internally built and directly adopted AI tools.